Big data algorithms beyond machine learning

Publikationstyp
Journal Article
Date Issued
2017-10-24
Sprache
English
Author(s)
Mnich, Matthias  
TORE-DOI
10.15480/882.3724
TORE-URI
http://hdl.handle.net/11420/10143
Journal
Künstliche Intelligenz  
Volume
32
Issue
1
Start Page
9
End Page
17
Citation
Künstliche Intelligenz 32 (1): 9-17 (2018)
Publisher DOI
10.1007/s13218-017-0517-5
Scopus ID
2-s2.0-85082998145
Publisher
Springer
Peer Reviewed
true
The availability of big data sets in research, industry and society in general has opened up many possibilities of how to use this data. In many applications, however, it is not the data itself that is of interest but rather we want to answer some question about it. These answers may sometimes be phrased as solutions to an optimization problem. We survey some algorithmic methods that optimize over large-scale data sets, beyond the realm of machine learning.
Subjects
Big data algorithms
Large-scale optimization
Kernelization
Dynamic algorithms
DDC Class
004: Informatik
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
Loading...
Thumbnail Image
Name

Mnich2018_Article_BigDataAlgorithmsBeyondMachine-2.pdf

Size

1.06 MB

Format

Adobe PDF